AI Recommendation Engine
The AI Recommendation Engine addresses the challenge of delivering high-quality personalized recommendations while maintaining performance, reliability, and explainability in real-world systems. Many modern platforms rely heavily on large language models to generate recommendations, which can introduce high latency, inconsistent outputs, and dependency on external services. At the same time, traditional similarity-based systems are fast and stable but often lack contextual understanding and deep personalization. This project bridges that gap by designing a recommendation platform that balances intelligent reasoning with deterministic reliability, ensuring consistent user experiences under a wide range of operating conditions.
The system is built as a full-stack recommendation platform for both movie and shopping domains. It combines LLM-based reasoning with a vector-based recommendation engine, allowing multiple models to be evaluated in parallel using a structured scoring framework. A Go-based backend orchestrates model execution, prompt construction, scoring, and result selection, while a vector engine provides fast similarity-based recommendations as a low-latency fallback. All recommendations are logged with detailed metrics for auditability and analysis, and results are presented through an interactive React frontend that supports model comparisons, batch processing, leaderboards, and CSV exports. This hybrid design enables the system to deliver personalized, explainable, and production-ready recommendations suitable for real-world deployment.